Processor for situational analysis

ABSTRACT

The present invention relates to a data-processing device, characterized in that it includes, in combination: a memory ( 10 ) which is organized into two spaces, one containing attributes describing the stored data, and the other containing connectivity links among the stored data, and which is organized according to one or more dimensions respectively associated with attributes and divided into one or more hierarchized segments, the data being informed by creating, deleting, or modifying attribute values characterizing said segments; a means ( 11 ) for integrating one or more incoming information streams, which analyzes said streams in order to determine the data constituting same and to structure said data and the attributes thereof according to the organization of said memory ( 10 ); an inference engine ( 14 ) implementing, in parallel or in series, inference rules ( 141 ) grouped together in libraries ( 15 ), said rules ( 141 ) being programmed to regenerate a sequence including at least one segment of the memory, by creating, deleting, or modifying at least one datum and/or at least one attribute value and/or at least one connectivity link during the implementation thereof in the at least one space of the memory; a means ( 13 ) for allocating resources activating or deactivating the inference rules ( 141 ) on the basis of a priority rule; a means ( 12 ) for extracting data, which are identified by programming and/or by at least one selected attribute value and/or at least one selected connectivity link, and/or which are located in one or more selected segments of the memory ( 10 ), and providing same in one or more outgoing information streams. The present invention also relates to an information-processing system and to an associated computer program product.

GENERAL TECHNICAL FIELD

The present invention relates to a data-processing device conceived forthe programming of expert systems or artificial intelligence systemssimulating human reasoning by being able to implement reasoning byinduction.

More precisely, it relates to a data-processing device dedicated inparticular to statistical processings, to datamining, to the conceptionof decision aid tools, to diagnostics, to forecasting or approximation,to the conception of simulators, automatic learning or aid to learningsystems and generally speaking the conception of situation analysissystems or situational analysis.

PRIOR ART

Computer processors have made it possible for more than 50 years toperform millions of calculations in always shorter times. With theirpower, they are more and more used as analysis and prediction tools.

However, the data-processing architecture implemented by processors haslimitations and constraints. A processor takes in input sequences ofbits, transforms them through series of logic gates, and deducestherefrom another sequence of bits in output. It is the series of logicgates that determines the calculation that the processor carries out.This series, predetermined in the program, depends on a modelimplemented by the user according to the rules of formal logic, andbased on known situations. These models may take extremely varied forms,from quantitative models for market finance up to mechanical models forengineering. The general principle consists in analyzing real phenomenato forecast results from the application of one or more models at agiven level of approximation.

Within the model, a processor proves to be effective, but remains on theone hand incapable of going beyond the limits of said model and alsoremains necessarily constrained by the limits inherent in the rules ofthe algorithmic logic. In addition, if the model has imperfections fromthe start, these will affect the quality of the results, or even therelevance of the model itself.

Another known approach has consisted in developing data-processingdevices with architecture inspired by the physical structure of thehuman brain in the form of artificial neuron network. This type ofarchitecture, the function of which is determined by the structure ofthe network, implements a learning process which makes it possible toacquire, to store and to use knowledge by using in particular theprinciple of induction by inference.

Different types of neuronal networks have been developed which haveproved in some cases to be more efficient than conventional processorarchitectures especially in the processing of signals and items ofinformation (telecommunications, finances, meteorology), processings ofstatistical nature (marketing), shape classification and recognition(imaging, character recognition).

These architectures produce however results that remain very dependenton the one hand on the structure of the network (the types of networksdiffer according to their topology, the aggregation functions and/orthresholding used) and on the other hand the learning model selected,which makes a phase of adjustment of the network necessary.

The present invention proposes an alternative data-processing devicewhich, through the observation of situations, makes it possible toanalyze data and to predict other situations, without being dependent ona model or on a physical or logical implementation structure.

“Situation” is here taken to mean more or less complex and more or lessvague information describing an action or a particular state. Thesituation comes from an observation by a human being or a machine andmay be as much real as imaginary.

DESCRIPTION OF THE INVENTION

The present invention proposes a data-processing device, itsarchitecture being able to be physical or logical and forming asituational analysis processor.

To this end a data-processing device is proposed, characterized in thatit comprises in combination:

-   -   a memory organized        -   on the one hand into two spaces, one containing attributes            describing the stored data, and the other containing            connexity links among the stored data,        -   and on the other hand according to one or more dimensions            respectively associated with attributes and divided into one            or more hierarchized segments, the data being inputted by            creating, deleting or modifying values of attributes            characterizing these segments;    -   means for integrating one or more incoming information streams        that analyze said streams to determine the data constituting        same and to structure said data and the attributes thereof        according to the organization of said memory;    -   an inference engine implementing, in parallel or in series,        inference rules grouped together in libraries, said rules being        programmed to regenerate a sequence including at least one        segment of the memory, by creating, deleting or modifying at        least one datum and/or at least one attribute value and/or at        least one connexity link during the implementation thereof in        the at least one space of the memory,    -   means for allocating resources activating or deactivating the        rules of inference on the basis of a priority rule,    -   means for extracting data, identified by programming and/or by        at least one selected attribute value and/or at least one        selected connexity link; and/or located in one or more selected        segments of the memory, and providing same in one or more        outgoing information streams.

According to other advantageous and non-limiting characteristics of theinvention:

-   -   the memory is organized in at least three dimensions, of which        at least one time dimension associating with the data one or        more attributes making it possible to date the memorization of        the data, an idiosyncratic dimension associating with the data        one or more attributes making it possible to determine the        relative specificity of the data, a conceptual dimension        associating with the data one or more attributes making it        possible to hierarchize the data according to levels of        abstraction;    -   the first space of the memory containing attributes of data is        constituted of even levels identified as levels of situations,        and the second space of the memory containing relations between        data is constituted of uneven levels identified as levels of        connexities;    -   at least one inference rule implemented by the inference engine        induces a connexity level of abstraction 2k+1 from a situation        level of abstraction 2k and/or a hypersituation level of        abstraction 2k+2 from a connexity level of abstraction 2k+1;    -   the priority rule implemented by the means for allocating        resources is a function of the hierarchy of the memory segments        to be regenerated, predefined parameters of the inference rules        and of the interval separating the activation occurrences of the        inference rules;    -   the memory is connected to a mass storage space in which        segments may be saved;    -   the means for extracting data select singular situations,        situations having varied, and plausible projected situations.

This innovative device opens up great prospects in numerous economicfields using analysis, prediction and simulation tools. It is capable,in an autonomous manner, of receiving in input one or more situationalstreams, of extracting situations therefrom, of distinguishing theimportant elements and continuously applying thereto processings,detecting phenomena and predicting evolutions of solutions, particularlyby induction.

The advantages are multiple: this device is not constrained by a modelor an implementation architecture, and thus permanently adapts itself.It is capable, following the example of the human brain, of focusing onthe essential by managing its resources. Finally, its possibilitiesappear much more universal than those of any current expert system,compartmentalized in a particular field.

The device according to the invention is asynchronous, nondeterministic, and proves to be neither connectionist nor computationistsince its power is both in its structure and its logic. It acceptssequential as parallel functioning.

The present invention proposes according to a second aspect a physicalimplementation of situational analysis processor, namely aninformation-processing system including at least one processing unit, atleast one memory unit, a first interface with at least one incominginformation stream and a second interface with at least one outgoinginformation stream, characterized in that it implements the architectureof the processing device according to the first aspect of the invention.

According to other advantageous and non-limiting characteristics of theinvention:

-   -   said processing unit comprises at least one multi-core        processor;    -   said processing unit implements a neuronal network structure.

The present invention proposes, according to a third aspect, a computerprogram product including program code instructions which, when they areexecuted, implement the architecture of the device according to thefirst aspect of the invention.

BRIEF DESCRIPTION OF DRAWINGS

Other characteristics and advantages of the present invention willbecome clear on reading the description that follows of a preferentialembodiment. This description will be given with reference to theappended drawings, in which:

FIG. 1 is a diagram of an embodiment of a device according to theinvention;

FIG. 2 is a diagram of an advantageous embodiment of a situationalmemory used by the invention;

FIG. 3 is a diagram showing how the device according to the inventionmay be integrated in a computer environment.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

As indicated above, the elementary unit in situational analysis is thesituation. This may be more or less true (for “past” situations), moreor less plausible (for “future” situations).

The situations are composed of information objects which are here named“situatrons”. Each situatron is a datum that can be manipulated and maybe present in one or more situations. It is described by a set ofattributes, the “clues”, which may be more or less precise or coherent.These attributes may evolve over time.

Thus, “Matthieu is drafting a patent application” is a situation. Thissituation may be described according to several points of view andaccording to different levels of detail. Thus, “The black mouse of thecomputer of Matthieu” also makes up a situation informing us on thegeneral situation. In this situation, the black mouse, the computer, thepatent application and Matthieu are situatrons.

At an even finer level of detail, the fact that the mouse is connectedto the computer and the fact that Matthieu possesses this computer makeup two particular situations from these same situatrons. The items ofinformation contained in these situations may be encoded in thefollowing manner:

-   OBJ(mouse001); situatron MOUSE-   IDC(colour, mouse001, black); attribute COLOUR black-   OBJ(computer001); situatron COMPUTER-   SIT(connected, mouse001, computer001); situation MOUSE connected to    the COMPUTER-   HUM(user001); situatron USER-   IDC(first name, user001, Matthieu); attribute FIRST NAME Matthieu-   SIT(use, computer001, user001); situation the USER Matthieu is using    the COMPUTER.

In this example it has been selected in an arbitrary manner that thecodes OBJ (Object) and HUM (Human) identify situatrons, while enablingthe distinction between objects and persons. The code IDC (clue) makesit possible to associate an attribute with a situatron and the code SIT(Situation) to describe a situation composed from previously definedsituatrons. The invention is nevertheless not limited to this syntax inparticular, and those skilled in the art will know how to adapt it,particularly by introducing other codes, for example SPA (space) whichwould designate a place.

The situations may thus not be encoded in natural language in order toescape the ambiguities inherent in standard vocabularies. The syntaxused to describe the situations is arbitrary but needs to be adapted tothe computer tools. Advantageously, the language selected is ametalanguage, in particular the language XML (Extensible MarkupLanguage).

The processing device 1 according to the invention is capable of workingon the situations and the situatrons described in this form.

What follows is an advantageous description of an embodiment of thisdevice 1, with reference to the diagram represented in FIG. 1. Itcomprises an organized memory 10, means 11 for integrating an incominginformation stream, an inference engine 14, means 13 for allocatingresources, means 12 for extracting data to an outgoing informationstream.

Memory

The memory 10 is a situational memory, structured to conserve the datanecessary for situational analysis. This memory 10 is organized into twospaces. The first space contains the attributes of the stored data, inother words directly of situations, situatrons, or groups of situationsor situatrons. The second space contains connexity links among thestored data, in other words the connexity relations between thesituations and situatrons of the first space.

The connexity links will be established from the moment that situatronswill be present in common situations, or share common clues. Thus, inthe preceding example, the fact of being connected to a computer createsa connexity link between different mice.

This second space is a dual space of the first, in fact it is an inducedfunctions space.

The situatrons, the attributes of a situatron and the connexity linksare thus induced from the observation and thus created as theinformation is acquired or a non observed situations are induced, theymay be deleted when they become obsolete, and they may be modified ifthey evolve or if they prove to be inexact. Their types are notpredefined, and may be adapted to any circumstance.

The memory 10 possesses a specific organization, an example of which isrepresented in FIG. 2. This organization follows one or more dimensions,respectively associated with attributes, and is divided into one or morehierarchized segments. Each segment is characterized by one or morevalues of attribute or ranges of particular values of attributes. Thedata are inputted by creating, deleting or modifying the values ofattributes characterizing these segments, which defined them a place inthe memory 10.

In particular, these dimensions are three in number in the preferredembodiment. This leads to an organization in the form of an assembly ofcubes 101, each cube being characterized by a unique combination of onesegment per dimension.

The first dimension is a time dimension, and makes it possible to datethe memorization of the data. The date of the memorization of the data(date of observation) remains separate from the date of the facts borneby the situation, the latter being able to be the object of one or moreseparate attributes, but without link with those of the time dimension.The segments of the time dimension correspond to periods. It extends onthe X axis in FIG. 2.

Thus, for example, the items of information relating to the Battle ofMarignan could be stored in the memory in the following manner:

-   OBS(30 Apr. 2010); date of the observation-   SIT(win a victory, king001, event001, place001); combat situation-   HUM(king001); actor situatron-   IDC(name, king001 “François 1^(st)”); clue associated with the    situatron-   EVT(event001); event situatron-   IDC(title, event001, “Battle of Marignan”); clue associated with the    situatron-   IDC(date, event001, 1515); clue associated with the situatron-   SPA(place001); place situatron-   IDC(country, place001, “Italy”); clue associated with the situatron

In this example it is possible to establish a time segmentation of thememory by defining the following periods:

-   -   Segment 1—Future=everything to come    -   Segment 2—Present=last 24 h    -   Segment 3—Recent past=3 preceding days    -   Segment 4—Intermediate past=in the last month    -   Segment 5—Distant past=remainder

Then, on the date of the 7 May 2010, the situational scene on the Battleof Marignan, observed on the 30 Apr. 2010, will be found again in thesegment 4.

If on the same day (7 May 2010) items of information on Marignan areintroduced which make it clear that the Swiss were defeated at thisbattle on the 13 September, they could themselves be stored in thefollowing manner:

-   OBS(7 May 2010);-   SIT(lose a battle, army001, event001);-   GRP(army001);-   IDC(name, army001, “Swiss”);-   IDC(date, event001, 13 Sep. 1515); modification of the clue

These new items of information are housed in the segment 2 and fullyforward the situatron event001 into the segment 2.

The breakdown of the memory into time segments thus makes it possible toconserve a “historical” image of each situation which may berepresented, in our example of Marignan, in the following manner:

Segment 2:

-   SIT(lose a battle, army001, event001)-   GRP(army001)-   IDC(name, army001, “Swiss”)-   EVT (event001)-   IDC(title, event001, “Battle of Marignan”)-   IDC(date, event001, 13 Sep. 1515)    Segment 4:-   SIT(win a victory, king001, event001, place001)-   HUM(king001)-   IDC(name, king001, “François 1st”)-   SPA(place001)-   IDC(country, place001, “Italy”)    -   the second dimension is an idiosyncratic dimension.        “Idiosyncratic” signifies “which constitutes the specificity,        the singularity of someone or something”. This dimension is in        fact linked to the level of incidence, which encompasses in a        general manner any criterion making a datum noteworthy, and in        particular the singularity or the relative specificity of the        datum. To return to our example of mice, an “orange mouse” will        be a situatron with a high singularity in a universe where        generally white mice are observed. This dimension extends along        the Y axis in FIG. 2.    -   the third dimension is a conceptual dimension. It associates        with the data one or more attributes making it possible to        hierarchize the data according to levels of abstraction. Each        level of abstraction defines a segment of the conceptual        dimension, which is called an abstraction plane 100. These        planes 100 extend along the Z axis and represent more and more        evolved forms of situation knowledge as the upper layers are        reached. Advantageously, the duality of the memory is        implemented in the form of an alternation of planes of        situations 100 a and planes of connexity 100 b: the set of even        levels 100 a is a partition of the space of situations, and the        set of uneven levels 100 b is a partition of the space of        connexity.    -   The level 0 contains situations, situatrons and groups of        situations and situatrons. Example: 2 black mice mouse001 and        mouse002; 2 computers comp001 and comp002; 2 users user001 and        user002; 2 situations “connected”; 2 situations “use”;    -   The level 1 contains connexity relations among the situations        and the situatrons of the first level. Continuing the preceding        example, it will involve, for the first series of situations and        situatrons, connexity links among: Mouse001 and comp001; Comp001        and user001; User001 and mouse001 (the latter link being        induced);    -   The level 2 contains hypersituations, hypersituatrons and groups        of hypersituations and hypersituatrons induced by an analysis of        the connexities of the second level. From the connexity link of        the lower level, it is possible to induce a hypersituatron MOUSE        or COMPUTER or USER, as well as hypersituations CONNECTED and        USE. At the level of hypersituatrons it is thus possible, for        example, to CONNECT the MOUSE to the computer and USE the        COMPUTER by the USER;    -   The level 3 the hyperconnexity that corresponds to the connexity        links established between the hypersituatrons and the        hypersituations of level 2;    -   And so on . . . .

Advantageously, the memory 10 is connected to a mass storage space 16 inwhich all or part of said memory may be saved. For example, in thepreferred embodiment in which the memory is organized according to threedimensions in the form of a cube assembly, it is memory cubes that willbe saved. This interaction with a mass storage space is advantageouslyimplemented in the form of swapping by those skilled in the art. Thismakes it possible to work both on several contexts by switching from oneto the other, or instead to work with situational memories of largersize than that of the addressing capacity of the processor.

Integrator

The device 1 comprises means 11 for integrating one or more incominginformation streams. These means 11 of integration (or situationalintegrator) recognize in the information stream(s) situations becomingcloser to situations already present in the memory 10. New situationsand new situatrons are produced from the data of the streams. These dataare structured and their attributes defined according to theorganization of the memory 10.

To take an example, the situational integrator could be a module thatreads lists of computer mouse sales presented in the form of XML streamson the input gates. It interprets the streams and separates the items ofinformation to:

-   -   Place the situations and the situatrons in the memory 10;    -   Place the instructions in the processing concentrator 13.

In the case of an embodiment in which the memory is divided into severaldimensions and segments, the integrator loads advantageously thesituations and the situatrons in:

-   -   The temporality segment containing the current date (“Present”);    -   The idiosyncratic segment of highest level of incidence;    -   The segment of abstraction determined according to the set up of        the integrator.        Inference Engine

Thanks to the integration means 11, the memory 10 contains data. Aninference engine 14 implements processings that are going to apply toall or part of the memory 10. These processings are loaded from alibrary 15 and are called inference rules 141. At each launch of rules,a sequence including at least one segment of memory is going to bescanned, and the associated memory regenerated. Regenerate is here takento mean create, delete or modify at least one datum and/or at least oneattribute value and/or at least one connexity link in at least one spaceof the memory. In particular, in the preferred embodiment describedhere, a processing that can be carried out by the inference engine 14 ona level of abstraction 100 may apply to levels of same parity.

Nevertheless any computer system has a finite capacity, and therecursive application of processings would rapidly take an exponentialtime. This is why each processing only concerns in general part of thememory 10. Segments are processed as a priority, then others accordingto the sequence described previously, before another processing nolonger becomes priority (see “concentrator” below). The processings canmoreover be executed in series as in parallel.

The inference rules 141 encompass all of the processings that it may beinteresting to apply. The selection of these rules 141 depends on theeffects sought, and may be judged by those skilled in the art as afunction of the application field for which the architecture isimplemented.

The following examples of possible rules 141 may be cited:

-   -   “Unification” consists in recognizing in a recent situatron an        older situatron or in a situation an already known situation and        to make the substitution and when appropriate the complement of        information through merger of elements.    -   “Temporalisation” consists in sliding the situations and the        situatrons concerned from one time segment to another.    -   “Reduction” consists in deleting clues of situatrons as they        slide into past segments. This operation is based on the        recorded use of clues.    -   “Trivialisation” consists in sliding situatrons and situations        between the levels of importance. The sliding may be in both        directions depending on whether the thing becomes trivial or        that its importance is reinforced given the evolutions of the        other situations.    -   “Aggregation” consists in gathering together several situatrons        that no longer need to be distinguished into a single aggregate        inheriting the principle properties of the situatrons and        replacing them by the aggregate each time that this is possible.    -   “Prediction” consists in applying situational phenomena on        observed situations in order to forecast forthcoming situations.    -   “Realization” consists in finding, in the present, situations        meeting the forecasts made and bringing them closer.    -   “Plausabilisation” consists in preferring in the forecast        situations those connected to past situations that have been        realized.

In particular, in the case of an embodiment with alternate abstractionlayers, a rule is used making it possible to induce a connexity level ofabstraction 2k+1 from a situation level of abstraction 2k and a rulemaking it possible to induce a hypersituation level of abstraction 2k+2from a connexity level of abstraction 2k+1, in other words generating ahigher level from the preceding.

For the first of these two rules, numerous possibilities exist, inparticular: the new connexity links may be deduced according to rules ofconnexity declared in the library, induced from the observation and thegeneralization of connexities from the past, statistical according tothe connexities distributed in the situational memory, probabilistic onBayesian sub-sets, genetic by mutation of previous connexity circles,etc.

The second of these two rules consists more simply in a generalizationof a group of situations and situatrons from a number of cases ofconnexities more or less important as a function of the level ofimportance.

Concentrator

Means 13 for allocating resources (or concentrator) manage theprocessing times between the different processings. To do this, theyhave available a priority rule according to which they activate ordeactivate the inference rules 141. There must never be too many rulesactive at the same time, at the risk of exceeding the capacity ofsystem.

The priority rule enables both a spatial hierarchy of the memory cubes101 to be processed, and a time hierarchy of the rules 141 to belaunched.

In one case as in the other, numerous parameters are taken into account.Advantageously, this breakdown takes place according to a criterionknown as “essentiality”. At any instant, the criterion of essentialityis calculated for a rule and the rule(s) are launched for which it ismaximal if they are not yet activated. In the case of launch of a rule,the essentiality criterion on the memory cubes 101 is then calculated(to be processed by said rule), the sequence is generated by sorting thesegments according to a decreasing essentiality, and the cubes areprocessed according to said sequence, it being interrupted in the caseof deactivation of the rule.

Firstly, the essentiality of the memory cubes depends on predefinedparameters inherent in the current rule. For example, the largest groupsof situations will be preferred for rules aiming to establish connexitylinks. Moreover, advantageously, the most abstract memory cubes, themost recent, and the most singular will have the maximal essentiality,also by analogy with human reflection.

Then, the essentiality of the rules depends also on predefinedparameters hierarchizing the rules, at equal context. But above all theessentiality of an inference rule 141 depends on the interval separatingits activation occurrences. The longer the rule has been inactive, thegreater its essentiality. This makes it possible to regularly carry outall sorts of processing.

In a preferential embodiment, the concentrator may be conceived as apile of processings in which the most essential processings are situatedat the top of the pile, the least essential at the bottom. The lastprocessing arrived places itself at the end of the pile with theessentiality of the “present” segment.

In this mode, the concentrator scans according to a fixed frequency theentire pile and recalculates at each cycle the level of essentiality ofthe processings as a function of different parameters. The followingparameters may for example be retained:

-   -   D: their waiting time. At each passage of the concentrator, D is        incremented by a fixed quantity, for example 0.25;    -   T: the temporality of the associated memory cube. T takes a        value representing the order of the temporality segment (from 1        to N, 1 characterizing the oldest segment);    -   I: the incidence of the associated memory cube. I takes a value        representing the order of the incidence segment (from 1 to N, 1        characterizing the segment of least incidence);    -   A: the level of abstraction of the associated memory cube. A        takes a value representing the order of the level of abstraction        (from 1 to N, 1 characterizing the lowest level).

The essentiality E may then advantageously be represented by thefollowing formula: E=D+I+T+A, or by any other formula of the type E=f(D,I, T, A), particularly E=d*D+i*I+t*T+a*A with (d, t, a) suitablecoefficients.

The invention is nevertheless not limited either to a specific mode ofcalculating the essentiality, or to a predefined list of parameters.

Moreover, in the embodiment described, the processings may becharacterized in order to control their mode of execution by theconcentrator. Thus, each processing may for example be defined as uniqueor as permanent: a unique processing will exit the pile when it isrealized whereas a permanent processing realized is replaced at the endof the pile and will be renewed. It may also be defined as active or asdelayed: an active processing is taken on by the concentrator whereas adelayed processing waits in the pile awaiting its turn, the concentratorignoring the delayed processings that have not come to an end. Apermanent delayed processing may also be defined as having to belaunched at a fixed date or according to a fixed frequency.

Extractor

A means 12 for extracting data (or extractor) make it possible todeliver an information stream thanks to which it will be possible tocollect the results of numerous processing cycles carried out by theinference engine 14 on the memory 10. The means 12 identify data withinthe memory 10 according to different principles, not mutually exclusive.Thus the data targeted may be defined according to attribute values, ortheir belonging to a closely related group. On the other hand, it istheir place in the memory that may be important, in this case the means12 of extraction listen to several memory cubes. The extraction criteriamay also be predefined arbitrarily by programming.

Advantageously, the information stream composed by the means 12 forextracting data is composed:

-   -   on the one hand of alerts: in this case, the means of extraction        are able to select singular situations (new, abnormal) or        situations having varied (situational phenomena);    -   and on the other hand case scenarios: the latter correspond to        projected predicted situations and present in the memory cubes        of plausible segments to come.        Physical Processor for Situational Analysis

The invention proposes according to a second aspect an informationprocessing system. It involves a physical implementation of thearchitecture of the processing device 1 according to the first aspect ofthe invention. This information processing system is for example in theform of a co-processor.

The processing system comprises a processing unit, dedicated to theexecution of rules 141 of inference. Advantageously, it is a multi-coreco-processor. In fact, as has been seen, situational analysis enablesthe parallel processing of data, with the possibility of activatingseveral rules simultaneously. Alternatively, this processing unit canimplement a neuronal network around several elementary processing“cells”. Such a network is particularly adapted to learning.

The processing system also comprises a memory unit, for example ofrandom access memory (RAM) type. It is there that will be hosted thememory 10 of the situational architecture. Optionally, the system alsoincludes a mass memory 16, such as a hard disc, to be able to carry outthe swapping of data described previously.

Two interfaces are also necessary, one for the incoming informationstream(s) intended for integration means 11, and the other for theoutgoing information stream(s) supplied by the means 12 for extractingdata.

Apart from this physical implementation, a co-processor for situationalanalysis may be purely logical, and be emulated as a program of a workstation. In this latter case, the invention relates to a computerprogram product including program code instructions which, when they areexecuted, implement the processor architecture for situational analysisdescribed previously.

Environment of the Processor for Situational Analysis

Whether physical or logical, the architecture of the device 1 fordata-processing by situational analysis is easily incorporated in otherarchitectures to produce efficient tools, as shown in FIG. 3. The means11 for integrating information may be adapted quite particularly to theinternet through which the data to be recovered are abundant. Forexample, the data may be generated by the course of one or more clientsin a store. It may be interesting to understand the situations in whichthey place themselves during a purchase, or precisely a non-purchase.

The situational analysis results, obtained via the means 12 forextracting data, may be transmitted to the client himself, for exampleto help him to make his choice: if he is looking for a computer mouse,the analysis of situations of previous purchasers may enable him todetermine a product that will suit him. The data may also obviously betransmitted and exploited by experts, in particular alerts on abnormalsituations.

The applications of situational analysis turn out nevertheless to beextremely vast, particularly in the world of the internet.

The invention claimed is:
 1. A data-processing device (1) comprising: amemory (10) organized into two spaces, a first space containingattributes describing the stored data, and a second space containingconnexity links among the stored data, and, according to one or moredimensions respectively associated with the attributes and divided intoone or more hierarchized segments, the data being inputted by creating,deleting or modifying the values of the attributes characterizing saidsegments; an integrator (11) for integrating one or more incominginformation streams, which analyzes said streams to determine the dataconstituting said streams and to structure said data and the attributesthereof according to the organization of said memory (10); an inferenceengine (14) implementing, in parallel or in series, inference rules(141) grouped together in libraries (15), said rules (141) beingprogrammed to regenerate a sequence including at least one segment ofthe memory, by creating, deleting or modifying at least one of a datum,an attribute value, and a connexity link during the implementationthereof in the at least one space of the memory, a resources allocator(13), which activates or deactivates the inference rules (141) on thebasis of a priority rule, a data extractor (12), which identifies databy programming and by at least one of a selected attribute value and aselected connexity link, located in one or more selected segments of thememory (10), and providing identified data in one or more outgoinginformation streams.
 2. The device according to claim 1, wherein thememory (10) is organized in at least three dimensions, of which at leastone time dimension associating with the data one or more attributesmaking it possible to date the memorization of the data, anidiosyncratic dimension associating with the data one or more attributesmaking it possible to determine the relative specificity of the data, aconceptual dimension associating with the data one or more attributesmaking it possible to hierarchize the data according to levels ofabstraction (100).
 3. The device according to claim 2, wherein the firstspace of the memory containing attributes of the data is constituted ofeven levels (100 a) identified as situation levels, and the second spaceof the memory containing relations between data is constituted of unevenlevels (100 b) identified as connexity levels.
 4. The device accordingto claim 1, wherein at least one inference rule implemented by theinference engine infers for a positive integer k a connexity level ofabstraction 2k+1 from at least one of a situation level of abstraction2k and a hypersituation level of abstraction 2k+2 from a connexity levelof abstraction 2k+1.
 5. The device according to claim 1, wherein thepriority rule implemented by the resource allocator (13) is a functionof the hierarchy of the memory segments to be regenerated, predefinedparameters of inference rules (141), and the interval separating theactivation occurrences of the inference rules (141).
 6. The deviceaccording to claim 1, wherein the memory is connected to a mass storagespace in which segments may be saved.
 7. The device according to claim1, wherein the data extractor (12) selects singular situations,situations having varied, and plausible projected situations.
 8. Aninformation-processing system including at least one processing unit, atleast one memory unit, a first interface with at least one incominginformation stream and a second interface with at least one outgoinginformation stream, which implements the architecture of the processingunit according to claim
 1. 9. The system according to claim 8, whereinsaid processing unit comprises at least one multi-core processor. 10.The system according to claim 8, wherein said processing unit implementsa neuronal network structure.